Next-Gen Banking: Integrating Machine Learning & Gen AI for Seamless User Experience & Robust Digital Footprint

By Nilesh Jahagirdar

With India’s vision to touch the $5 trillion economy, the govt. is simultaneously aim to reach digital economy to become USD1 trillion by 2025 as per a report cited by the Institute of development & research in Banking Technology.

Amidst being in a VUCA (volatility, uncertainty, complexity, and ambiguity) world from which no sectors are safe, the banking industry has made a remarkable transformation shift driven by the widespread adoption of digital technology. Over the last decade, India has experienced a surge of technological innovations driven by its sophisticated IT industry and the country’s demographic strengths, making it the world’s second-largest digital ecosystem aided by the government’s commitment to digitizing key facets of the economy, innovations in the private sector, and investments to stimulate the use and access of the internet. The government has

Also with the fast paced evolution of banking apps and the rise of digital banking, financial institutions are increasingly leveraging machine learning (ML) to provide seamless user experiences and build robust digital footprints.

It’s imperative for banks to be AI-first.

There is no doubt, that the advancements in the technology field have taken the world by storm and emerged as a powerful tool that is revolutionizing the banking sector.

It is widely acknowledged that we are currently in the era of AI-driven digital advancements, facilitated by declining costs in data storage and processing, improved accessibility and connectivity for all, and rapid progress in AI technologies. These advancements have the potential to significantly automate processes and, when implemented cautiously, often surpass human decision-making in terms of both speed and accuracy. The scope for creating value is immense across various industries, with AI potentially unlocking an additional $1 trillion in value for banks annually.

ML, a subset of AI, further helps in revolutionizing the way banking services are delivered and consumed by analyzing vast amounts of data and identifying patterns; ML algorithms also enable banks to offer personalized experiences tailored to individual needs, enhance security measures, and optimize marketing strategies. Gen-AI, another trendsetting subset of AI, offers a myriad of advantages by analyzing extensive data sets, recognizing complex patterns, and extracting valuable insights that could transform how decisions are made. Gen AI also plays a vital role in fraud detection, risk evaluation, and tailoring customer interactions for improved efficiency and precision. Its applications span algorithmic trading, credit risk analysis, and the development of customer service chatbots, showcasing its diverse and substantial impact on digital banking practices.

Enhancing User Experience with ML & Gen AI

Currently, the global banking market is earmarked to exceed $6,256 million by 2032, a significant jump from its 2022 value of $865 million with an expected compound annual growth rate (CAGR) of 22.5% from 2023 to 2032, citing the financial sector is primed for a revolutionary shift driven by the adoption of generative AI.

Leveraging AI tools like Gen AI also helped in achieving a productivity boost ranging from 22% to 30%. However, the real transformation lies in its impact on revenue. By integrating AI with human efforts in sales, marketing, and customer interactions, there’s a possibility of unlocking a remarkable 6% increase in new revenue streams within the next three years.

Through ML algorithms, banks can offer customized financial advice, suggest relevant products and services, and anticipate user needs based on their transaction history and behavior. It further coupled with 24/7 AI backed Chat-bots can help address customer queries, provide account information, and assist with transactions, thereby improving accessibility and convenience for users.

In the VUCA world, AI tools are a gamechanger because of its abilities to detect anomalies in real-time and assisted with ML algorithms further help banks identify and mitigate fraudulent activities, safeguarding the financial assets and sensitive information of customers.

Need for a Robust Digital Footprint

To streamline the banking process and improving customer engagement and retention, ML is almost indispensable as it aids banks in understanding customer behavior and preferences through sophisticated data analysis of transaction history, spending patterns, and browsing habits, enabling tailored offerings and eventually help banks maintain a competitive edge.

Roadblock to combat

As per the research conducted by the National Business Research Institute and Narrative Science, it was found about 32% of financial service providers in India are already using AI technologies such as predictive analytics and voice recognition, denoting that the Indian banking sector is increasingly adopting AI by the day. Banks such as SBI, Bank of Baroda, HDFC, ICICI, Yes Bank, and others are already deploying AI to streamline their regular processes.

According to a study, India surpassed the global average of 79% with a staggering 83% towards its anticipation of the collaboration between AI & humans within the next two years, citing its confidence in AI’s dominance. However, the same report also observed that 77% of Indians agreed they must adeptly create and/or integrate AI tools into banking services as it too comes with its fair of challenges like the following:-

•         Cost of Training Manpower
•         Understanding the significance of data standardization
•         Differing enforcement approaches
•         User Capacity
•         Reading & decoding Multiple languages
•         Data protection & Privacy issues

It is also plagued by technical challenges, including data integration, model deployment, and scalability, which require careful consideration to ensure smooth implementation.

But on the bright side, there have been case studies of leading banks demonstrating the successful integration of ML into their apps. For example, Bank of America’s virtual financial assistant, Erica, utilizes ML algorithms to provide personalized insights and assist customers with financial management tasks, enhancing the overall user experience.

Road Ahead

Looking ahead, the future of ML in digital banking holds exciting possibilities, from predictive analytics for credit risk assessment to natural language processing for customer support. By staying abreast of technological advancements and embracing innovation, banks can unlock new opportunities and stay ahead in today’s competitive landscape.

Finding the right balance of implementing effective physical strategies, existing technologies, industry collaboration, and the adoption of innovative technologies, the resilient banking sector is poised to meet customer demands and contribute to the nation’s financial stability as it enters a new era.

(The author is Nilesh Jahagirdar, Co- Founder & VP of Marketing & Solutions at [x]cube LABS, and the views expressed in this article are his own)